1887
PDF

Abstract

This paper presents novel applications of the Bayesian inversion approach and the simulated annealing method (SAM) for identifying and reducing uncertainties involved in automatic history matching and forecasting processes which are of paramount importance for optimisation of hydrocarbon recovery. The Bayesian inversion approach enables the priori knowledge about the inversion parameters to be incorporated into the objective function to form a posteriori, in conjunction with the likelihood function reflecting the mismatch between the history data and the data predicted from the numerical model. The simulated annealing method (SAM) bas been applied to escape local optima which may still exist in the posteriori objective function, in order to further reduce the nonuniqueness of the inversion solutions.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201406762
1997-10-20
2021-06-24
Loading full text...

Full text loading...

http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201406762
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error